Sleeping hours: what is the ideal number and how does age impact this?
Bibliographic record
Abstract
The objective of this narrative review paper is to discuss about sleep duration needed across the lifespan. Sleep duration varies widely across the lifespan and shows an inverse relationship with age. Sleep duration recommendations issued by public health authorities are important for surveillance and help to inform the population of interventions, policies, and healthy sleep behaviors. However, the ideal amount of sleep required each night can vary between different individuals due to genetic factors and other reasons, and it is important to adapt our recommendations on a case-by-case basis. Sleep duration recommendations (public health approach) are well suited to provide guidance at the population-level standpoint, while advice at the individual level (eg, in clinic) should be individualized to the reality of each person. A generally valid assumption is that individuals obtain the right amount of sleep if they wake up feeling well rested and perform well during the day. Beyond sleep quantity, other important sleep characteristics should be considered such as sleep quality and sleep timing (bedtime and wake-up time). In conclusion, the important inter-individual variability in sleep needs across the life cycle implies that there is no "magic number" for the ideal duration of sleep. However, it is important to continue to promote sleep health for all. Sleep is not a waste of time and should receive the same level of attention as nutrition and exercise in the package for good health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".